In this mini-workshop we will introduce the sf package, show some examples of geospatial analysis, work with base plotting of sf objects, and show how mapview can be used to map these objects. It is assumed that you have R and RStudio installed and that you, at a minimum, understand the basic concepts of the R language (e.g. you can work throughR For Cats).

Also as an aside, I am learning the sf package right now so, we will be learning all of this together!

The `sf` package

Things are changing quickly in the R/spatial analysis world and the most fundamental change is via the sf package. This package aims to replace sp, rgdal, and rgeos. There are a lot of reasons why this is a good thing, but that is a bit beyond the scope of this workshop Suffice it to say it should make things faster and simpler!

To get started, lets get `sf installed:

install.packages("sf")
library("sf")

It does rely on having access the GDAL, GEOS, and Proj.4 libraries. On Windows and Mac this should be pretty straightforward.

Exercise 1

The first exercise won’t be too thrilling, but we need to make sure everyone has the packages installed.

Install sf.
Load sf.
If you don’t have dplyr already, make sure it is installed.
Load dplyr.

Reading in spatial data with `sf`

Simple Features

So, what does sf actually provide us? It is an implementation of an ISO standard for storing spatial data. It forms the basis for many of the common vector data models and is centered on the concept of a “feature”. Essentially a feature is any object in the real world. There are many different types of features and there are different details that get stored about each. For details on this the first sf vignette does a really nice job. For this mini-workshop we are going to focus on three feature types, POINT, LINESTRING, and POLYGON. For each of the types, there will be coordinates stored as dimensions, a coordinate reference system, and attributes.

Get some data to use

We can grab some data directly from the Rhode Island Geographic Information System (RIGIS) for these examples. This code assumes you have a data folder in your current workspace.

# Municipal Boundaries
download.file(url = "http://www.rigis.org/geodata/bnd/muni97d.zip",
              destfile = "data/muni97d.zip")
unzip(zipfile = "data/muni97d.zip", 
      exdir = "data")

# Streams
download.file(url = "http://www.rigis.org/geodata/hydro/streams.zip",
              destfile = "data/streams.zip")
unzip(zipfile = "data/streams.zip", 
      exdir = "data")

# Potential Growth Centers
download.file(url = "http://www.rigis.org/geodata/plan/growth06.zip",
              destfile = "data/growth06.zip")
unzip(zipfile = "data/growth06.zip", 
      exdir = "data")

# Land Use/Land Cover
download.file(url = "http://www.rigis.org/geodata/plan/rilc11d.zip",
              destfile = "data/rilc11d.zip")
unzip(zipfile = "data/rilc11d.zip", 
      exdir = "data")

Data input

To pull the shapefiles in we can simply use the st_read() function. This will create an object which is a simple feature collection of, in our case, POINT, LINESTRING, or POLYGON. As an aside, many of the sf functions and all of the ones we will be using start with st_. This stands for “spatial” and “temporal”. Take a look below for examples reading in each of our datasets.

POINT

growth_cent <- st_read("data/growth06.shp")

## Reading layer `growth06' from data source `/data/jhollist/geospatial_with_sf/data/growth06.shp' using driver `ESRI Shapefile'
## Simple feature collection with 21 features and 2 fields
## geometry type:  POINT
## dimension:      XY
## bbox:           xmin: 260137.3 ymin: 32916.7 xmax: 418116.3 ymax: 326549.2
## epsg (SRID):    NA
## proj4string:    +proj=tmerc +lat_0=41.08333333333334 +lon_0=-71.5 +k=0.99999375 +x_0=99999.99999999999 +y_0=0 +datum=NAD83 +units=us-ft +no_defs

LINESTRING

streams <- st_read("data/streams.shp")

## Reading layer `streams' from data source `/data/jhollist/geospatial_with_sf/data/streams.shp' using driver `ESRI Shapefile'
## Simple feature collection with 4470 features and 8 fields
## geometry type:  LINESTRING
## dimension:      XY
## bbox:           xmin: 234010.1 ymin: 31361.37 xmax: 430921.9 ymax: 340865.8
## epsg (SRID):    NA
## proj4string:    +proj=tmerc +lat_0=41.08333333333334 +lon_0=-71.5 +k=0.99999375 +x_0=99999.99999999999 +y_0=0 +datum=NAD83 +units=us-ft +no_defs

POLYGON

muni <- st_read("data/muni97d.shp")

## Reading layer `muni97d' from data source `/data/jhollist/geospatial_with_sf/data/muni97d.shp' using driver `ESRI Shapefile'
## Simple feature collection with 396 features and 12 fields
## geometry type:  POLYGON
## dimension:      XY
## bbox:           xmin: 220310.4 ymin: 23048.49 xmax: 432040.9 ymax: 340916.6
## epsg (SRID):    NA
## proj4string:    +proj=tmerc +lat_0=41.08333333333334 +lon_0=-71.5 +k=0.99999375 +x_0=99999.99999999999 +y_0=0 +datum=NAD83 +units=us-ft +no_defs

lulc <- st_read("data/rilc11d.shp")

## Reading layer `rilc11d' from data source `/data/jhollist/geospatial_with_sf/data/rilc11d.shp' using driver `ESRI Shapefile'
## Simple feature collection with 68186 features and 5 fields
## geometry type:  POLYGON
## dimension:      XY
## bbox:           xmin: 218380.4 ymin: 23048.5 xmax: 434592 ymax: 343508
## epsg (SRID):    NA
## proj4string:    +proj=tmerc +lat_0=41.08333333333334 +lon_0=-71.5 +k=0.99999375 +x_0=99999.99999999999 +y_0=0 +datum=NAD83 +units=us-ft +no_defs

A word on performance

One of the benefits of using sf is the speed. In my tests it is about twice as fast as the prior standard of sp and rgdal. Let’s look at a biggish shape file with 1 million points!

1 million points

#The old way
system.time(rgdal::readOGR("data","big"))

## OGR data source with driver: ESRI Shapefile 
## Source: "data", layer: "big"
## with 1000000 features
## It has 1 fields

##    user  system elapsed 
##  10.758   0.138  10.909

#The sf way
system.time(st_read("data/big.shp"))

## Reading layer `big' from data source `/data/jhollist/geospatial_with_sf/data/big.shp' using driver `ESRI Shapefile'
## Simple feature collection with 1000000 features and 1 field
## geometry type:  POINT
## dimension:      XY
## bbox:           xmin: -71.03768 ymin: 41.05976 xmax: -69.09763 ymax: 43.00856
## epsg (SRID):    4326
## proj4string:    +proj=longlat +datum=WGS84 +no_defs

##    user  system elapsed 
##   5.244   0.097   5.343

Exercise 2

Make sure each of the shapefiles is read in. Follow the instructions from above to complete this.

Basics of `sf` objects

One of the nicest features (pun intended) about sf objects is that they are nothing more than data frames. The data for each feature (e.g. the attributes in ESRI speak) are stored in the data frames first columns. The last column of the data frame is a “geometry” column which holds the coordinates, and coordinate reference system information. I say this is nice becuase we don’t need to completely learn a new way of working with spatial data. Much of what we now about working with plain old tabular data frames will also work with sf objects.

We can use many of our base R skills directly with these objects.

head(muni)

## Simple feature collection with 6 features and 12 fields
## geometry type:  POLYGON
## dimension:      XY
## bbox:           xmin: 246748.8 ymin: 282524.2 xmax: 360358 ymax: 340916.6
## epsg (SRID):    NA
## proj4string:    +proj=tmerc +lat_0=41.08333333333334 +lon_0=-71.5 +k=0.99999375 +x_0=99999.99999999999 +y_0=0 +datum=NAD83 +units=us-ft +no_defs
##         AREA PERIMETER RITOWN5K_ RITOWN5K_I             NAME MCD CFIPS
## 1  788769737 134174.19         2        380       CUMBERLAND  20     7
## 2  219887686  61203.98         3          1       WOONSOCKET  80     7
## 3  693661121 122732.51         4          2 NORTH SMITHFIELD  55     7
## 4 1588026224 166984.85         5          3     BURRILLVILLE   5     7
## 5  527562041 117805.34         6        381          LINCOLN  45     7
## 6  769680403 111353.48         7          4       SMITHFIELD  75     7
##       COUNTY OSP CFIPS_MCD TWNCODE LAND                       geometry
## 1 PROVIDENCE   8      7020      CU    Y POLYGON((339469.65625 34051...
## 2 PROVIDENCE  39      7080      WO    Y POLYGON((320285.09375 33947...
## 3 PROVIDENCE  25      7055      NS    Y POLYGON((299086.65625 33886...
## 4 PROVIDENCE   3      7005      BU    Y POLYGON((303493.28125 31006...
## 5 PROVIDENCE  17      7045      LI    Y POLYGON((331461.675 320529....
## 6 PROVIDENCE  31      7075      SM    Y POLYGON((303493.28125 31006...

streams$NAME[1:10]

##  [1] Mill River      No Name         Round Top Brook Round Top Brook
##  [5] Peters River    Indian Brook    Chockalog River Chockalog River
##  [9] Chockalog River Chockalog River
## 242 Levels: Abbott Run Brook Acid Factory Brook ... Woonasquatucket River

str(growth_cent)

## Classes 'sf' and 'data.frame':   21 obs. of  3 variables:
##  $ NAME      : Factor w/ 21 levels "BLOCK ISLAND",..: 8 5 14 9 21 4 3 17 13 20 ...
##  $ Cntr_Class: Factor w/ 2 levels "Town","Village": 2 2 2 2 2 2 2 2 2 2 ...
##  $ geometry  : List of  21 , printing Classes 'XY', 'POINT', 'sfg'  num [1:2] 279907 321759
##  - attr(*, "sf_column")= chr "geometry"
##  - attr(*, "agr")= Factor w/ 3 levels "constant","aggregate",..: NA NA
##   ..- attr(*, "names")= chr  "NAME" "Cntr_Class"

Manipulate `sf` objects with `dplyr`, yes, `dplyr`!

However, what is truly awesome, is the ability for us to now use dplyr to manipulate our spatial data and pipe our spatial workflows. First let’s make sure dplyr is loaded up.

library("dplyr")

Now we can work with our spatial data. Let’s filter out the towns that are more than

big_muni <- muni %>%
  mutate(area_skm = AREA*.000000092903) %>%
  group_by(NAME) %>%
  summarize(sum_area_skm = sum(area_skm)) %>%
  filter(sum_area_skm >= 100)
big_muni

## Simple feature collection with 11 features and 2 fields
## geometry type:  GEOMETRY
## dimension:      XY
## bbox:           xmin: 220310.4 ymin: 23048.49 xmax: 432040.9 ymax: 340916.6
## epsg (SRID):    NA
## proj4string:    +proj=tmerc +lat_0=41.08333333333334 +lon_0=-71.5 +k=0.99999375 +x_0=99999.99999999999 +y_0=0 +datum=NAD83 +units=us-ft +no_defs
## # A tibble: 11 × 3
##               NAME sum_area_skm          geometry
##             <fctr>        <dbl>  <simple_feature>
## 1     BURRILLVILLE     147.5324 <POLYGON((303...>
## 2         COVENTRY     161.7605 <POLYGON((315...>
## 3           EXETER     151.2190 <POLYGON((315...>
## 4           FOSTER     134.6021 <POLYGON((276...>
## 5        GLOCESTER     147.1955 <POLYGON((303...>
## 6        HOPKINTON     114.3237 <POLYGON((248...>
## 7  NORTH KINGSTOWN     114.1386 <MULTIPOLYGON...>
## 8         RICHMOND     105.5525 <POLYGON((297...>
## 9         SCITUATE     141.9332 <POLYGON((276...>
## 10 SOUTH KINGSTOWN     155.9371 <MULTIPOLYGON...>
## 11  WEST GREENWICH     132.6521 <POLYGON((315...>

And first time I tried this, this happened:

mind_blown

Exercise 3

Let’s try the same thing with our land use/land cover data, except without the filter. Use dplyr and

Add a column to convert area from acres to square kilometers
Group by LULC_2011
Sum up the square kilometers

Plotting

There are default plotting methods for these sf objects, so we can use our base plotting tools to create some quick maps.

Spatial analysis in R with the `sf` package

The `sf` package

Exercise 1

Reading in spatial data with `sf`

Simple Features

Get some data to use

Data input

POINT

LINESTRING

POLYGON

A word on performance

Exercise 2

Basics of `sf` objects

Manipulate `sf` objects with `dplyr`, yes, `dplyr`!

Exercise 3

Plotting

Base

Mapview

Exercise 4

Analysis

Buffer and summarize

Exercise 5

Spatial analysis in R with the sf package

The sf package

Exercise 1

Reading in spatial data with sf

Simple Features

Get some data to use

Data input

POINT

LINESTRING

POLYGON

A word on performance

Exercise 2

Basics of sf objects

Manipulate sf objects with dplyr, yes, dplyr!

Exercise 3

Plotting

Base

Mapview

Exercise 4

Analysis

Buffer and summarize

Exercise 5

Spatial analysis in R with the `sf` package

The `sf` package

Reading in spatial data with `sf`

Basics of `sf` objects

Manipulate `sf` objects with `dplyr`, yes, `dplyr`!